This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly
proposed masked self-distillation into contrastive language-image pretraining. The core idea …

The combination of strong visual backbones and Large Language Model (LLM) reasoning
has led to Large Multimodal Models (LMMs) becoming the current standard for a wide range …

The scarcity of data presents a critical obstacle to the efficacy of medical vision-language pre-
training (VLP). A potential solution lies in the combination of datasets from various language …

Abstract Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet
effective way to train large-scale vision-language models. CLIP demonstrates impressive …

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in
a variety of tasks. However, some aspects of complex language understanding still remain a …

Vision-Language models like CLIP have been widely adopted for various tasks due to their
impressive zero-shot capabilities. However, CLIP is not suitable for extracting 3D geometric …

Large-scale pre-trained Vision & Language (VL) models have shown remarkable
performance in many applications, enabling replacing a fixed set of supported classes with …

Abstract Out-of-distribution (OOD) generalization, where the model needs to handle
distribution shifts from training, is a major challenge of machine learning. Contrastive …